基于ALOS遥感影像的黄土丘陵区典型流域景观制图及格局研究
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摘要
以黄土高原丘陵沟壑区的典型小流域燕沟为研究对象,基于野外现地勾绘和基于遥感影像的方法制得燕沟土地景观图,研究了燕沟流域当前的景观格局、景观斑块多样性和异质性、景观功能类型、土地利用合理性和近20年的土地利用变化,确定了燕沟流域的景观格局特征和土地利用的特点及演变趋势,探索了基于面向对象分类方法在制取黄土高原小流域土地景观图的途径,提出了优化燕沟景观格局提高土地利用合理性的途径,为黄土高原水土流失区域植被景观优化配置提供了科学依据。
主要研究结果如下:
(1)燕沟流域当前的景观格局特征。流域景观斑块共2679块,斑块分维数为1.0855,表明该流域景观类型的形状简单,景观格局稳定。该区主要景观类型为灌丛和林地,分别约占流域面积的43%和19%。辽东栎(Quercus liaotungensis Koidz)林地是流域内仅存的天然林地,分布于流域沟掌处。草地的平均斑块面积为0.0041km2,斑块数量众多,在流域景观破碎度上起着主要贡献。耕地和果园的斑块密度分别为51和25个/km~2,对流域景观的破碎化起到了缓冲作用。
(2)燕沟流域的景观基质是生态防护型景观。生态防护型景观的斑块数为总斑块数的3/4,面积约占总流域面积的70%,生产经济型景观斑块在流域中零星镶嵌,斑块数约为生态防护型景观斑块的1/4,面积占流域面积的近1/3。生活服务型景观约占流域面积的3%。生态防护型景观、生产经济型景观和生活服务型景观的面积比约为23:9:1,其斑块平均周长分别约为663m、1028m和472 m。
(3)燕沟流域土地利用整体合理。燕沟土地利用的整体合理度为0.76。局部区域的土地利用合理度情况为:阴坡为0.94,在各坡向中为最合理,半阴坡最不合理,为0.74。在海拔小于900时,土地利用合理度仅为0.02,在900～1000米时,为0.998,合理度最高。海拔大于900m时,土地利用合理度随着海拔的增加而逐渐降低。
(4)燕沟流域景观近20年的显著变化特点是灌丛、林地和果园面积大幅度增加。燕沟的主要景观类型为灌丛和林地,并且主要分布在大于25°的坡面上,流域景观分维度为1.0855,表明景观格局稳定。20年来,林地和果园面积增加了近一倍,灌丛增加了约11%。荒坡沟草地面积减少,居民地面积增加了1%。燕沟流域治理初期(2000年)和治理后期(2007年)的主导景观均为灌丛,治理初期,分布在陡峭坡面(大于25°)的土地景观类型主要是封禁的荒坡地,而在治理的后期,则主要为灌丛和林地。
(5)基于面向对象分类的方法能高效、高精度制取燕沟土地利用图,为适合该区的土地景观图制取方法。在所采用的土地景观图的制取方法中,基于面向对象分类方法制取的燕沟土地利用图的精度最高,为77.73%,Kappa值为0.7144;野外现地勾绘结果的精度为37.89%,Kappa值为0.1935;监督分类的精度为59.38%,Kappa值为0.4937;其中,目视解译方法较费时费力,所得流域景观斑块比较破碎。现地勾绘所得的土地景观类型多达24种,而其他几种分类方法所得的土地景观类型仅为灌丛、林地、草地、农田、果园、居住地和水体7类。
(6)不同景观制图方法对三大景观功能类型斑块和面积比例的影响。监督分类结果的景观斑块总数为10,447,约为目视解译所得斑块总数(2679块)的4倍,远大于野外勾绘(920块)和基于面向对象分类(900块)结果的总斑块数。并且,几种分类方法所得的生态防护型、生产经济型和生活服务型三大景观的面积比例情况:野外现地勾绘,21:10:1;目视解译,23:9:1;监督分类,8:7:4.6;基于面向对象分类,23:13:1。
(7)基于面向对象分类适地添加数字高程模型(DEM)和归一化植被指数(NDVI)辅助数据提高分类效果。这两种辅助数据可以增加地形和植对该区土地利用类型布局的影响,还增加了高分辨率全色立体影像在面向对象影像分割和分类中的比重,利用不同景观类型的分类阈值,并设置影像的分割尺度为150,颜色/形状比值为0.9/0.1,紧密度/平滑度比值为0.5/0.5时,分割效果就最好。
(8)不同分类方法对燕沟土地景观图的面积扩展影响不同。基于面向对象分类方法和目视解译方法所得的各个土地景观类型面积分布近似。监督分类方法对灌丛、农田、居住地和水体所分布的面积较多。野外勾绘对于具有较大斑块的林地、果园和易于与农田混淆的草地分配了较多的面积。几种方法的空间面积扩展小于2%,并且都能将自然的土地利用类型分出,且灌丛为流域的主导景观。
This study revealed the landscape spatial pattern of the comprehensive management in loess hilly -gully watershed, to supply the reference for further optimization of the watershed landscape. Also to explore the accurate efficient landscape map mapping approach. This study took the typical loess hilly and gully Yan’gou watershed as an example, the field delineation and the remote sensing imagery based mapping approaches were employed to get the landscape map of Yan’gou watershed. Based on the landscape maps, analyzed the status landscape pattern, landscape patch diversity and heterogeneity, landscape function types, land use suitability and the land use change during near the past 20 years.
Determined the landscape pattern features, the land use characteristics of the landscape pattern and land use evolution trends, explored the object-based classification as the accurate and efficient approach to classify the loess hilly and gully watershed satellite imagery. The way of further optimize the landscape pattern of Yan’gou watershe was proposed.
The main results were as follows:
(1) The current landscape pattern of Yan’gou watershed. The total landscape patche was 2679 and the patch fractal dimension was 1.0855, indicating that the shape of the watershed landscape was simple and stability. The main landscape pattern were bush and forest, took 43.32% and 19.17% of the total watershed area respectively. Robur (Quercus liaotungensis Koidz.) forest was the only remaining natural woodland, located at the palm of the watershed. The average area of the grass patch was 0.0041 km2, and the large number of the grass patch played a major contribution for the landscape fragmentation in the watershed. The patch density of the farmland and the orchard were 51 and 25 respetively which played a buffer role for the watershed landscape fragmentation.
(2) The dominant landscape type was ecological protection landscape. Its patches took more than 3/4 of the total landscape patches, and it took 70% of the watershed area. Economic production landscape patches scattered in the watershed, and its patches took 1/4 of the total patches and area took 1/3 of the total area. Living-service landscape type took about 3% of the watershed area. The area ratio of the three landscape function types, ecological protection, economic production and living-service landscape, was about 23:9:1, and the mean patch perimeter were about 663m, 1028m and 472m respectively, which means that the landscape patches of ecological protection and living-service were small and broken, while the economic production patches presents better unity under more human intervention.
(3) The suitability of the overall landscape distribution in Yan’gou was relatively reasonable. The land-use suitability of Yan’gou was 0.76. The suitability of parts of Yan’gou area were: the highest land use suitability was the shaded aspect with value of 0.94. The land use on the semi-shady aspect was the most unreasonable with the suitability of 0.74; the suitability was 0.02 in the area which had altitude less than 900m, and the highest value in all the elevation levels was 0.998 for altitude of 900～1000m. When the altitude was more than 900m, the suitability of the elevation decreased with altitude increasing.
(4) The main character of the changing in the past 20 years is significant increases occurred in bush, forest and orchard.Forest and the orchard nearly doubled the area, and bush increased by about 11%. Grass was significantly reduced the distribution area. The residential area increased by nearly 1 percentage point. The dominant landscape type of Yan’gou was bush in both the initial (2000) and late (2007) stages of the management. In 2000, the wild protection area was the dominant landscape type on the slopes with more than 25 degrees, however, bushes and woodlands were dominant in 2007.
(5) The object-based classification could efficiently abstract the information in the imagery with high accuracy, it’s the suitable approach for mapping the landscape map in hilly-gully area. The object-based classification results got the hightest accuracy among the approaches, the object-based classification approach got the highest classification accuracy was up to77.73%, Kappa value was 0.7144; Yan’gou land use/cover maps obtained from the field survey and the supervised classification approaches got relative lower classification accuracy and kappa values, which were 37.89 %, kappa 0.1935, and 59.38%, kappa 0.4937 respectively. The firld survey results had as many as 24 categories of land cover types, however, other classification approaches that employed in this study such as visual interpretation, supervised classification and object-based classifications all abstracted 7 landscape types which were bush, forest, grass, farmland, orchard, residential area and water.
(6) The approaches affected the landscape patch and function differently. The total landscape patches of the results from the supervised classification was 10,447, which was about 4 times more than the results that was from visual interpretation (2679), and much more than the landscape patches that was from the field survey and the object-based classification (920 and 900 respectively), The area ratio of the three landscape function types which were ecological protection, economic production and living-service in the difference classification approach were: for the field survey, 21:10:1; the visual interpretation, 23:9:1; supervised classification, 8:7:4.6; object-based classifiction, 23:13:1.
(7) Auxiliary data of DEM and the NDVI of Yan’gou watershed were employed to add to the object-based segmentation to improve the classification accuracy. The auxiliary data information affected the land use/cover in the watershed. The panchromatic imagery was weighted in the object-based segmentation and classification. With the land use/cover classification threshold values and suitable segmentation scales of 150, and the color/Shape value was 0.9/0.1, compactness/Smoothness value was 0.5/0.5, got the best segmentation results.
(8) Different classification approaches affected the expansion of Yan’gou land use/cover. Results from the object-based classification and visual interpretation had smaller area differences between the land use/cover respectively. Area variance in supervised classification methods was mainly for the bush, farmland, residential area and water. Field survey was mainly for woodland patches with large area, orchard and grasslands. Whereas all of the land use types obtained from the four approaches had shown spatial area extension (less than 2%), all the methods had been separated the natural land use/cover types and there were no huge differences of the spatial area among the approaches. All the methods could classified the bush as the dominant land use/cover type in Yan’gou watershed.
主要研究结果如下:
(1)燕沟流域当前的景观格局特征。流域景观斑块共2679块,斑块分维数为1.0855,表明该流域景观类型的形状简单,景观格局稳定。该区主要景观类型为灌丛和林地,分别约占流域面积的43%和19%。辽东栎(Quercus liaotungensis Koidz)林地是流域内仅存的天然林地,分布于流域沟掌处。草地的平均斑块面积为0.0041km2,斑块数量众多,在流域景观破碎度上起着主要贡献。耕地和果园的斑块密度分别为51和25个/km~2,对流域景观的破碎化起到了缓冲作用。
(2)燕沟流域的景观基质是生态防护型景观。生态防护型景观的斑块数为总斑块数的3/4,面积约占总流域面积的70%,生产经济型景观斑块在流域中零星镶嵌,斑块数约为生态防护型景观斑块的1/4,面积占流域面积的近1/3。生活服务型景观约占流域面积的3%。生态防护型景观、生产经济型景观和生活服务型景观的面积比约为23:9:1,其斑块平均周长分别约为663m、1028m和472 m。
(3)燕沟流域土地利用整体合理。燕沟土地利用的整体合理度为0.76。局部区域的土地利用合理度情况为:阴坡为0.94,在各坡向中为最合理,半阴坡最不合理,为0.74。在海拔小于900时,土地利用合理度仅为0.02,在900～1000米时,为0.998,合理度最高。海拔大于900m时,土地利用合理度随着海拔的增加而逐渐降低。
(4)燕沟流域景观近20年的显著变化特点是灌丛、林地和果园面积大幅度增加。燕沟的主要景观类型为灌丛和林地,并且主要分布在大于25°的坡面上,流域景观分维度为1.0855,表明景观格局稳定。20年来,林地和果园面积增加了近一倍,灌丛增加了约11%。荒坡沟草地面积减少,居民地面积增加了1%。燕沟流域治理初期(2000年)和治理后期(2007年)的主导景观均为灌丛,治理初期,分布在陡峭坡面(大于25°)的土地景观类型主要是封禁的荒坡地,而在治理的后期,则主要为灌丛和林地。
(5)基于面向对象分类的方法能高效、高精度制取燕沟土地利用图,为适合该区的土地景观图制取方法。在所采用的土地景观图的制取方法中,基于面向对象分类方法制取的燕沟土地利用图的精度最高,为77.73%,Kappa值为0.7144;野外现地勾绘结果的精度为37.89%,Kappa值为0.1935;监督分类的精度为59.38%,Kappa值为0.4937;其中,目视解译方法较费时费力,所得流域景观斑块比较破碎。现地勾绘所得的土地景观类型多达24种,而其他几种分类方法所得的土地景观类型仅为灌丛、林地、草地、农田、果园、居住地和水体7类。
(6)不同景观制图方法对三大景观功能类型斑块和面积比例的影响。监督分类结果的景观斑块总数为10,447,约为目视解译所得斑块总数(2679块)的4倍,远大于野外勾绘(920块)和基于面向对象分类(900块)结果的总斑块数。并且,几种分类方法所得的生态防护型、生产经济型和生活服务型三大景观的面积比例情况:野外现地勾绘,21:10:1;目视解译,23:9:1;监督分类,8:7:4.6;基于面向对象分类,23:13:1。
(7)基于面向对象分类适地添加数字高程模型(DEM)和归一化植被指数(NDVI)辅助数据提高分类效果。这两种辅助数据可以增加地形和植对该区土地利用类型布局的影响,还增加了高分辨率全色立体影像在面向对象影像分割和分类中的比重,利用不同景观类型的分类阈值,并设置影像的分割尺度为150,颜色/形状比值为0.9/0.1,紧密度/平滑度比值为0.5/0.5时,分割效果就最好。
(8)不同分类方法对燕沟土地景观图的面积扩展影响不同。基于面向对象分类方法和目视解译方法所得的各个土地景观类型面积分布近似。监督分类方法对灌丛、农田、居住地和水体所分布的面积较多。野外勾绘对于具有较大斑块的林地、果园和易于与农田混淆的草地分配了较多的面积。几种方法的空间面积扩展小于2%,并且都能将自然的土地利用类型分出,且灌丛为流域的主导景观。
This study revealed the landscape spatial pattern of the comprehensive management in loess hilly -gully watershed, to supply the reference for further optimization of the watershed landscape. Also to explore the accurate efficient landscape map mapping approach. This study took the typical loess hilly and gully Yan’gou watershed as an example, the field delineation and the remote sensing imagery based mapping approaches were employed to get the landscape map of Yan’gou watershed. Based on the landscape maps, analyzed the status landscape pattern, landscape patch diversity and heterogeneity, landscape function types, land use suitability and the land use change during near the past 20 years.
Determined the landscape pattern features, the land use characteristics of the landscape pattern and land use evolution trends, explored the object-based classification as the accurate and efficient approach to classify the loess hilly and gully watershed satellite imagery. The way of further optimize the landscape pattern of Yan’gou watershe was proposed.
The main results were as follows:
(1) The current landscape pattern of Yan’gou watershed. The total landscape patche was 2679 and the patch fractal dimension was 1.0855, indicating that the shape of the watershed landscape was simple and stability. The main landscape pattern were bush and forest, took 43.32% and 19.17% of the total watershed area respectively. Robur (Quercus liaotungensis Koidz.) forest was the only remaining natural woodland, located at the palm of the watershed. The average area of the grass patch was 0.0041 km2, and the large number of the grass patch played a major contribution for the landscape fragmentation in the watershed. The patch density of the farmland and the orchard were 51 and 25 respetively which played a buffer role for the watershed landscape fragmentation.
(2) The dominant landscape type was ecological protection landscape. Its patches took more than 3/4 of the total landscape patches, and it took 70% of the watershed area. Economic production landscape patches scattered in the watershed, and its patches took 1/4 of the total patches and area took 1/3 of the total area. Living-service landscape type took about 3% of the watershed area. The area ratio of the three landscape function types, ecological protection, economic production and living-service landscape, was about 23:9:1, and the mean patch perimeter were about 663m, 1028m and 472m respectively, which means that the landscape patches of ecological protection and living-service were small and broken, while the economic production patches presents better unity under more human intervention.
(3) The suitability of the overall landscape distribution in Yan’gou was relatively reasonable. The land-use suitability of Yan’gou was 0.76. The suitability of parts of Yan’gou area were: the highest land use suitability was the shaded aspect with value of 0.94. The land use on the semi-shady aspect was the most unreasonable with the suitability of 0.74; the suitability was 0.02 in the area which had altitude less than 900m, and the highest value in all the elevation levels was 0.998 for altitude of 900～1000m. When the altitude was more than 900m, the suitability of the elevation decreased with altitude increasing.
(4) The main character of the changing in the past 20 years is significant increases occurred in bush, forest and orchard.Forest and the orchard nearly doubled the area, and bush increased by about 11%. Grass was significantly reduced the distribution area. The residential area increased by nearly 1 percentage point. The dominant landscape type of Yan’gou was bush in both the initial (2000) and late (2007) stages of the management. In 2000, the wild protection area was the dominant landscape type on the slopes with more than 25 degrees, however, bushes and woodlands were dominant in 2007.
(5) The object-based classification could efficiently abstract the information in the imagery with high accuracy, it’s the suitable approach for mapping the landscape map in hilly-gully area. The object-based classification results got the hightest accuracy among the approaches, the object-based classification approach got the highest classification accuracy was up to77.73%, Kappa value was 0.7144; Yan’gou land use/cover maps obtained from the field survey and the supervised classification approaches got relative lower classification accuracy and kappa values, which were 37.89 %, kappa 0.1935, and 59.38%, kappa 0.4937 respectively. The firld survey results had as many as 24 categories of land cover types, however, other classification approaches that employed in this study such as visual interpretation, supervised classification and object-based classifications all abstracted 7 landscape types which were bush, forest, grass, farmland, orchard, residential area and water.
(6) The approaches affected the landscape patch and function differently. The total landscape patches of the results from the supervised classification was 10,447, which was about 4 times more than the results that was from visual interpretation (2679), and much more than the landscape patches that was from the field survey and the object-based classification (920 and 900 respectively), The area ratio of the three landscape function types which were ecological protection, economic production and living-service in the difference classification approach were: for the field survey, 21:10:1; the visual interpretation, 23:9:1; supervised classification, 8:7:4.6; object-based classifiction, 23:13:1.
(7) Auxiliary data of DEM and the NDVI of Yan’gou watershed were employed to add to the object-based segmentation to improve the classification accuracy. The auxiliary data information affected the land use/cover in the watershed. The panchromatic imagery was weighted in the object-based segmentation and classification. With the land use/cover classification threshold values and suitable segmentation scales of 150, and the color/Shape value was 0.9/0.1, compactness/Smoothness value was 0.5/0.5, got the best segmentation results.
(8) Different classification approaches affected the expansion of Yan’gou land use/cover. Results from the object-based classification and visual interpretation had smaller area differences between the land use/cover respectively. Area variance in supervised classification methods was mainly for the bush, farmland, residential area and water. Field survey was mainly for woodland patches with large area, orchard and grasslands. Whereas all of the land use types obtained from the four approaches had shown spatial area extension (less than 2%), all the methods had been separated the natural land use/cover types and there were no huge differences of the spatial area among the approaches. All the methods could classified the bush as the dominant land use/cover type in Yan’gou watershed.
引文
包维楷,陈庆恒. 1998.退化山地植被恢复和重建的基本理论和方法.长江流域资源与环境. 7 (4): 370-377
边肇祺,张学工. 2000.模式识别.北京:清华大学出版社
蔡红艳,张树文,张宇博. 2010.全球环境变化视角下的土地覆盖分类系统研究综述.遥感技术与应用, 25(1): 161-167
曹雪,柯长青. 2006.基于对象级的高分辨率遥感影像分类研究.遥感信息, (5): 27-30
陈国建,张晓萍,张平仓,李锐,杨勤科. 2009.基于RS和GIS的六道沟流域土地利用动态变化研究.水土保持研究, 16(2): 95-100
陈靓. 2006.景观生态学研究中的格局分析方法及模型.安庆师范学院学报, 12(3): 13-16
陈静谊,张志强,王新杰,唐丽霞. 2008.黄土高原清水河流域植被景观动态变化研究.水土保持研究, 15(5): 53-56
陈利顶,傅伯杰,王军. 2001.黄土丘陵区典型小流域土地利用变化研究——以陕西延安地区大南沟流域为例.地理科学, 21(1): 46-51
陈利顶,吕一河,傅伯杰. 2006.基于模式识别的景观格局分析与尺度转换研究框架.生态学报, 26 (3): 663-670
陈述彭,赵英时. 1990.遥感地学分析,测绘出版社
陈忠. 2006.高分辨率遥感图像分类技术研究.中国科学院遥感应用研究所,博士论文: 6-7
戴昌达,姜小光,唐伶俐. 2004.遥感图像应用处理与分析.北京:清华大学出版社
邓毅,蔡凌. 2009.基于可持续性的城市景观格局规划概念与方法.城镇景观, 95-101
杜凤兰,田庆久,夏学齐,慧凤鸣. 2004.面向对象的地物分类法分析与评价.遥感技术与应用, 19(1): 20-24
冯春,陈建. 2003.平土地利用/土地覆盖研究中遥感图像分类精度的提高方法.地理与地理信息科学, 19(3): 26-28
傅伯杰,陈利顶,马克明,王仰麟. 2001.景观生态学原理及应用.北京:科学出版社
傅伯杰,陈利顶,马克明. 1999.黄土丘陵区小流域土地利用变化对生态环境的影响.地理学报, 54(3): 241-246
傅伯杰,陈利顶,邱扬. 1988.黄土丘陵沟壑区土地利用结构与生态过程.北京:科学出版社, 185-232
傅伯杰,陈利顶. 1997.土地持续利用评价的指标体系与方法.自然资源学报, 12(2): 112-119
傅伯杰,吕一河,陈利顶,苏常红,姚雪玲,刘宇. 2008.国际景观生态学研究新进展.生态学报, 28(2): 798-804
傅伯杰,马克明,周华峰,陈利顶. 1998.黄土丘陵区土地利用结构对土壤养分分布的影响.科学通报, 43(22): 244-247
傅伯杰. 1995.黄土区穿凿景观空间格局分析.生态学报, 15(2): 113-120
富伟,刘世梁,崔保山,张兆苓. 2009.基于景观格局与过程的云南省典型地区道路网络生态效应.应用生态学报, 20(8): 1925-1931
高欣,常庆瑞,卞德鹏,刘炜,杨昆. 2010.陕北黄土丘陵沟壑区ALOS遥感影像融合方法研究.西北农林科技大学学报, 38(3): 189-193
高杨,吴志峰,刘晓南,张景华. 2008.珠江三角洲景观空间格局分析.热带地理, 28(1): 26-31
高照良,穆兴民. 2004.水土保持学报.黄土水蚀风蚀交错区土地利用覆被时空变化研究—以陕西省神木县六道沟流域为例, 18(5): 146-150
高志强,宁吉才,高炜. 2009.基于遥感的沿海土地利用变化及地表温度响应.农业工程学报, 25(9): 274-281
宫攀,陈仲新,唐华俊,张凤荣,张明伟. 2006.基于MODIS温度/植被指数的东北地区土地覆盖分类.农业工程学报, 22(9): 94-99
宫攀,唐华俊,陈仲新,张凤荣. 2006.基于MODISLST修正NDVI时序数列的土地覆盖分类.资源科学, 28(4): 104-110
宫鹏. 2009.基于全球通量观测站的全球土地覆盖图精度检验.自然科学进展, 19(7): 754-759
顾娟,李新,黄春林. 2010.基于时序MODISNDVI的黑河流域土地覆盖分类研究.地球科学进展, 25(3): 317-326
郭泺,夏北成,刘蔚秋. 2006.地形因子对森林景观格局多尺度效应分析.生态学杂志, 25(8): 900- 904
韩书成,谢永生,濮励杰.2006.黄土高原沟壑区小流域土地利用特征变化分析—以长武王东沟为例.干旱区资源与环境, 20(4): 73-77
郝润梅,海春兴,雷军. 2006.农牧交错带农田景观格局对土地生态环境安全的影响—以呼和浩特市为例.干旱区地理, 29(5): 700-704
郝仕龙,陈南详,柯俊. 2005.黄土丘陵小流域土地利用景观空间格局动态分析.农业工程学报, 21(6): 50-53
何彬方,范伟,刘惠敏,冯妍. 2007.基于MODIS数据的安徽省土地覆盖分类研究.红外技术, 29(11): 651-656
何东进,洪滔,胡海清,王英姿,刘勇生,范圣锋. 2007.武夷山风景名胜区不同森林景观物种多样性特征研究.中国生态农业学报, 15(2): 9-13
侯伟,鲁学军,张春晓,王静. 2010.面向对象的高分辨率影像信息提取方法研究—以四川理县居民地提取为例.地球信息科学学报, 12(1): 119-125
胡宝清,金姝兰,曹少英,蒋树芳. 2004.基于GIS技术的广西喀斯特生态环境脆弱性综合评价.水土保持学报, 18(1): 103-107
胡相明,程积民,万惠娥,赵艳云. 2006.黄土丘陵区地形、土壤水分与草地的景观格局.生态学报, 26 (10): 3276-3285
黄慧萍,吴炳方,李苗苗,周为峰,王忠武. 2004.高分辨率影像城市绿地快速提取技术与应用.遥感学报, 8(1): 68-74
黄慧萍. 2003.面向对象影像分析中的尺度问题研究.北京:中国科学院研究生院
黄奕龙,陈利顶,傅伯杰,王仰麟. 2005.黄土丘陵小流域土壤水分空间格局及其影响因素.自然资源学报, 20(4): 483-492
贾宁凤,段建南,乔志敏. 2007.土地利用空间分布与地形因子相关性分析方法.经济地理, 27(2): 310-312
贾永红,张春森,王爱平. 2001.基于BP神经网络的多源遥感影像分类.西安科技学院学报, 21(1): 58-60
蒋定生. 1997.黄土高原水土流失与治理模式.中国水利水电出版社
琚彤军,刘普灵. 2001.燕沟流域水保治理措施优化配置示范研究.水土保持学报, 15(5): 55-57
琚彤军,王建军,刘普灵,郑世清. 2002.燕沟示范区生态农业快速规模化建设途径分析.西北农林科技大学学报, 2(5): 9-13
孔宁宁,曾辉,李书娟. 2002.四川卧龙自然保护区植被的地形分异格局研究.北京大学学报, 38(4), 543-549
李爱农,江小波,马泽忠,周爱霞. 2003.遥感自动分类在西南地区土地利用调查中的应用研究.遥感技术与应用, 18(5): 282-285
李斌,张金屯. 2009.黄土高原灌丛景观斑块形状的指数和分形分析.中国农学通报, 25(22): 296-300
李朝峰. 2004.基于知识发现和决策规则的遥感图像城区土地覆盖/利用分类方法.计算机工程与应用, 23: 212-215
李春华,沙晋明. 2006.基于知识的遥感影像的BAYES分类方法研究—以福州市琅歧岛土地覆盖/土地利用类型为例.水土保持研究, 13(6): 126-128
李敏,崔世勇,李成名,印洁,李云岭. 2008.面向对象的高分辨率遥感影像信息提取—以耕地提取为例.遥感应用, 6:63-89
李团胜. 2005.基于遥感数据的晋陕蒙交汇区景观格局定量分析—以东胜幅1/25万T M影像分析为例.生态学杂志, 24(9): 1033-1037
李小利,刘国彬,张晓萍,孟庆香. 2008.黄土丘陵油田开发区景观破碎化分析.中国水土保持科学, 6(3): 53-58
林皓波,柏延臣,王锦地,贾秀萍,尤淑撑. 2009.基于景观结构的遥感影像超分辨率制图.自然科学进展, 19(12): 1355-1364
林洋.2009.燕沟流域景观格局现状、功能与动态研究. [硕士论文西北].陕西杨凌.农林科技大学
林洋,张文辉. 2010.黄土丘陵沟壑燕沟流域景观构成及功能类型.西北农林科技大学学报, 38(1): 145-160
刘爱霞,王静,吕春艳. 2006.基于MODIS数据的北京西北部地区土地覆盖分类研究.地理科学进展, 25(2): 96-102
刘常娟,杨敏华,张秀英. 2009.面向对象的分类方法在土地利用数据库建设中的应用.测绘科学, 34(1): 176-177
刘芳,郝玉光,张景波,汪守海. 2009.磴口县土地景观变化遥感监测研究.内蒙古农业大学学报, 30(3): 112-116
刘纪远. 1996.中国资源环境遥感宏观调查与动态研究,中国科学技术出版社
刘建飞,杨勤科,梁伟,任宗萍,胡志瑞. 2009.近30年来陕北黄土高原土地利用动态变化分析.水土保持研究, 16(2): 112-116
刘璞,张远,周斌,吴嘉平. 2009.基于SAM和多源信息的土地利用/覆盖自动分类.浙江大学学报, 43(9): 1574-1579
刘普灵,郑世清,琚彤军,王栓全,徐勇. 2007.黄土高原燕沟流域生态环境建设模式及效益研究.中国生态农业学报, 15(3): 175-178
刘庆凤,刘吉平,宋开山. 2010.基于MODIS/NDVI时序数据的土地覆盖分类.中国科学院研究生院学报, 27(2): 163-169
刘仁钊,廖文峰. 2005.遥感图像分类应用研究综述.地理空间信息, 3(5): 1l-13
刘伟,冯学智,肖鹏峰,徐海根. 2006.基于MODIS数据的生物保护区域土地覆被分类.江西师范大学学报, 30(2): 201-205
刘伟,高艳红,李海英,冉有华,程国栋, Chen F. 2007.黑河流域土地覆盖分类数据的建立及其影响的模拟.高原气象, 26(4): 278-285
刘文娟,常庆瑞,贾科利,刘京,刘梦云. 2007.荒漠化地区土地覆被光谱特征与遥感信息提取方法研究.西北农林科技大学学报, 35(9): 205-209
刘文耀,李昆. 1999.南涧干热河谷退化山地植被恢复重建的研究.北京林业大学学报, 21(3): 9-13
刘贤赵,谭春英. 2005.黄土高原沟壑区典型小流域土地利用变化对产水量的影响—以陕西省长武王东沟流域为例.中国生态农业学报, 13(4): 99-102
刘玉洁,杨忠东. 2001. MODIS遥感信息处理原理与算法.科学出版社
刘志刚,史文中,李德仁,秦前清. 2005.一种基于支撑向量机的遥感影像不完全监督分类新方法.遥感学报, 9(4): 363-373
梁伟,杨勤科,刘咏梅. 2006.黄土高原地形复杂区土地利用信息提取方法研究.国土资源遥感, 17(3): 56-60
陆大明,范建友. 2007.基于面向对象影像分类技术的生态清洁小流域现状调查.中国水土保持, 9: 23-26
罗海江,白海玲,王文杰,申文明,张峰,刘小曼. 2006.面向生态监测与管理的国家级土地生态分类方案研究.中国环境监测, 22(10): 57-60
罗来平. 2006.遥感图像分类中模糊模式识别和决策树方法的应用研究.北京:首都师范大学
骆成凤,刘正军,王长耀,牛铮. 2006.基于遗传算法优化的BP神经网络遥感数据土地覆盖分类.农业工程学报, 22(12): 133-137
骆剑承,周成虎,梁怡,马江洪. 2002.支撑向量机及其遥感影像空间特征提取和分类的应用研究,遥感学报, 6(1): 50-55
买凯乐,张文辉. 2011a.黄土丘陵沟壑区遥感影像信息面向对象分类方法提取.农业机械学, 42(4): 169-175
买凯乐,张文辉. 2011b.基于ALOS影像的黄土丘陵区典型流域景观空间格局.应用生态学报, 22(3): 741-747
孟平,贾宝全,张劲松,高峻. 2004.太行山低山丘陵区景观变化特征分析—以河南省济源市为例.生态学报, 24(4): 825-830
莫登奎,林辉,孙华,熊育久,刘秀英. 2005.基于高分辨率遥感影像的土地覆盖信息提取.遥感技术与应用, 20(4): 411-414
莫源富,周立新. 2000.分区分类法针对山区遥感图象的一种有效的分类方法,中国岩溶, 19(4): 360-365
潘竟虎. 2010.基于光谱混合模型的城市绿色空间格局分析—以兰州市为例.城市问题, 2: 18-23
彭海涛,柯长青. 2010.基于多层分割的面向对象遥感影像分类方法研究.遥感技术与应用, 25(1): 149-154
彭望碌,白振平,刘湘南,曹彤. 2002.遥感概论.北京:高等教育出版社
蒲智,刘萍,杨辽,葛挺进. 2006.面向对象技术在城市绿地信息提取中的应用.福建林业科, 33(1): 40-44
邱扬,张金屯. 2000. DCCA排序轴分类及其在关帝山八水沟植物群落生态梯度分析中的应用.生态学报, 20(2): 199-206
Roberto V,周月琴. 1997.影像理解的智能系统.测绘通报, 1: 40-41
容丽,王世杰,杜雪莲. 2006.喀斯特低热河谷石漠化区环境梯度的小气候效应—以贵州花江峡谷区小流域为例.生态学杂志, 25(9): 1038-1043
申广荣,钱振华,徐敬敬,李艳. 2009.基于eCognition的城镇绿地信息动态监测研究.上海交通大学学报, 27(1): 1-6
沈泽昊,赵俊. 2007.基于植物-地形关系的物种丰富度空间格局预测—GAMs途径的一种应用.生态学报, 27(3): 953-963生态学名词审定委员会. 2006.生态学名词.北京:科学出版社
宋冬梅,肖笃宁,张志诚,曹宇,马明国,王建华,王建. 2003.甘肃民勤绿洲的景观格局变化及驱动力分析.应用生态学报, 14(4): 535-539
宋永昌. 2001.植被生态学.上海:华东师范大学出版社
孙丹峰,杨冀红,刘顺喜. 2002.高分辨率遥感卫星影像在土地利用分类及其变化监测的应用研究.农业工程学报. 18(2): 160-164
孙建国,杨树文,段焕娥,王沛,闫浩文. 2009.基于光谱和纹理特征的山区高分辨率遥感影像分类.测绘科学, 3(6): 92-93
孙艳玲,杨小唤,王新生,陈建军. 2007.基于决策树和MODIS数据的土地利用分类.资源科学, 29(5): 169-174
孙艳敏,林培. 2005.县级中大比例尺以土地类型划分为基础进行农用地分等的方法论研究—以太原市万柏林区为例.中国农业科学, 38(7): 1408-1413
索安宁,熊友才,王天明,寇晓军,葛剑平. 2007.黄土高原子午岭森林破碎化对流域水文过程的影响.林业科学, 43(6): 13-19
索安宁,周睿,洪军,葛剑平. 2005.基于遥感和GIS的黄土高原小流域植被退化研究.北京师范大学学报, 41(2): 181-184
覃先林,增元,易浩若. 2005.高空间分辨率卫星遥感影像树冠信息提取方法研究.遥感技术与应用, 20(2): 228-232
田均良,刘普灵,张翼. 2000.治理水土流失再造山川秀美延安.水土保持研究, 7(2):4-9
田新光. 2007.面向对象高分辨率遥感影像信息提取.中国测绘科学研究院
田野,方涛. 2010.基于ART2技术的面向对象高分辨遥感影像分类.计算机应用与软件, 27(4): 48-50
万幼川,宋杨. 2005.基于高分辨率遥感影像分类的地图更新方法.武汉大学学报, 30(2): 105-109
汪闽,万其明,张大骞,张青峰. 2010.光谱、形状特征结合的多精度图像分割算法与应用.地球信息科学学报, 12(2): 261-268
王长耀,牛铮,唐华俊. 2001.对地观测技术与精细农业.科学出版社
王惠英,苏伟,周军其. 2009.基于多分类器组合面向对象遥感影像信息提取方法.测绘工程, 18(5): 22-26
王莉雯,牛铮,卫亚星. 2007.基于MODISNDVI的新疆土地覆盖类型划分与潜在荒漠化区域探测.干旱区地理, 30(4): 573-578
王礼先. 1999.流域管理学.北京:中国林业出版社
王文宇,李博. 2006.基于eCogniton的高分辨率遥感图像的自动识别分类技术.北京建筑工程学院学报, 22(4): 26-29
王晓燕,徐志高,杨明义,田均良. 2004.黄土高原小流域景观多样性动态分析.应用生态学报, 15(2): 273-277
王永军,李团胜. 2006.基于GIS的榆林地区景观格局动态变化.生态学杂志, 25(8): 859-899
瓦普尼克. 2000.统计学习理论的本质.北京:清华大学出版社
魏静,郑小刚,葛京凤. 2007.石家庄西部太行山区景观格局时空变化.生态学报, 27(5): 1993-2001
温仲明,焦峰,张晓萍,杨勤科. 2004.纸坊沟流域近60年来土地利用景观变化的环境效应.生态学报, 24(9): 1903-1909
温仲明,焦峰,赫晓慧,杨勤科,刘宝元. 2006.纸坊沟流域黄土丘陵区土地生产力变化与生态环境改善.农业工程学报. 22(8): 91-95
温仲明,焦峰,张晓萍,杨勤科. 2004a.黄土丘陵区纸坊沟流域60年来土地利用格局变化研究.水土保持学报, 18(5): 125-133
温仲明,焦峰,张晓萍,杨勤科. 2004b.纸坊沟流域近60年来土地利用景观变化的环境效应.生态学报, 24(9): 1903-1909
邬建国. 2000a.景观生态学—格局、过程、尺度与等级.北京:高等教育出版社, 19-31
邬建国. 2000b.景观生态学—概念与理论.生态学杂志, 19(1): 42-52
吴连喜,饶月辉. 2009.一种用于土地利用调查的颜色保真的遥感图像融合方法.土壤通报, 40(4): 731-735
吴文斌,杨鹏,张莉,唐华俊,周清波, Shibasaki R. 2009.四类全球土地覆盖数据在中国区域的精度
评价.农业工程学报, 25(12): 167-173
夏俊士,杜培军,张海荣,刘培. 2010.基于遥感数据的城市地表温度与土地覆盖定量研究.遥感技术与应用, 25(1): 15-23
肖笃宁. 1991.景观生态学:理论、方法及应用.北京:中国林业出版社
谢芳,邱国玉,尹婧,熊育久,王佩. 2009.泾河流域40年的土地利用/覆盖变化分区对比研究.自然资源学报, 24(8): 1354-1365
徐永明,刘勇洪,魏鸣,吕晶晶. 2007.基于MODIS数据的长江三角洲地区土地覆盖分类.地理学报, 62(6): 640-648
徐勇, Roy C S. 2001.黄土高原丘陵区燕沟流域土地利用变化与优化调控.地理学报, 56(6): 175-178
徐勇,马定国,郭腾云. 2004.黄土丘陵区的生态环境建设宜走“梯田退耕”的路子.水土保持研究, 11(3): 112-115
徐勇,田均良,刘普灵. 2002.黄土高原中部丘陵区生态适宜型农村经济发展模式案例研究.水土保持通报, 22(3): 43-46
徐勇,田均良,刘普灵. 2004.黄土丘陵区梯田退耕生态重建规划方法—以燕沟流域为例.自然资源学报, 19(5): 637-646
徐勇,许炯心,房金福. 2000.黄上高原中部丘陵区农村经济特征、制约因素与发展对策.水十保持研究, 7(2): 14-21
许晓波,张婷,张文辉. 2009.纸坊沟流域近25年景观斑块功能类型及动态.林业科学, 45(5): 154-160
薛莲,金卫斌,熊勤学,刘章勇. 2010.基于MODIS和ENVISAT数据的湖北省四湖地区土地覆盖分类.应用生态学报, 21(3): 791-795
薛智德,杨光,梁一民. 1997.燕沟人工植被营造模式与快速建设研究.水土保持研究, 7(2): 128-132
严海英. 2008. QUICKBIRD影像用于城市用地信息提取方法.测绘科学, 33(2): 216-217
杨娟,葛剑平,洪军. 2006.卧龙地区流域土地覆盖变化及其对大熊猫潜在生境的影响.生态学报, 26(6): 1975-1980
杨朴,史明昌,黄兆伟,李团宏. 2009.面向对象的黄土高原坝系拓扑关系数据模型研究.中国水土保持, 5: 36-43
杨叶涛,龚建雅,周启鸣,王迎迎. 2010.土地利用景观格局对城市扩张影响研究.自然资源学报,25(2): 320-329
姚凤梅,张佳华. 2006.全球及区域模式中陆面过程的地表植被覆盖分类方法.干旱区地理, 29(6): 872-877
姚静,武文波,康停军. 2010.基于TM影像的城市绿地信息提取方法研究.测绘科学, 35(1): 113-115
游珍,李占斌. 2007.小流域人类活动的时空分异的景观格局特征—以纸坊沟流域为例.干旱区资源与环境, 21(3): 89-94
余新晓,张晓明,牛丽丽,岳永杰,武思宏,张满良. 2009.黄土高原流域土地利用/覆被动态演变及驱动力分析.农业工程学报, 25(7): 219-225
俞来雷. 2010.国家历史文化名城土地覆盖研究.城市问题, 4: 15-62
张翠萍,牛建明,董建军,李旻. 2006.基于IKONOS数据的植被制图与植被空间格局—以五分地沟试验区为例.生态学报, 26(2): 449-456
张洪亮. 2000.一种基于GIS的森林分类专家系统(FCGES)理论与方法.云南地理环境研究. 12(1): 54-58
张锦水,潘耀忠,韩立建,苏伟,何春阳. 2007.光谱与纹理信息复合的土地利用/覆盖变化动态监测研究.遥感学报, 11(4): 500-510
张景,姚凤梅,徐永明,张佳华. 2010.基于MODIS的土地覆盖遥感分类特征的评价与比较.地理科学, 30(2): 248-253
张路,廖明生. 2006.一种顾及上下文的遥感影像模糊聚类.遥感学报, 10(1): 58-61
张明阳,王克林,何萍,谢志仁,高吉喜. 2005.白洋淀流域景观空间格局变化研究.资源科学, 27(2): 134-140
张秋玲,马金辉,赵传燕. 2007.兴隆山地区景观格局变化及驱动因子.生态学报, 27(8): 3206-3214
张树华,王百田. 2007.晋西黄土丘陵县域景观格局分析.水土保持研究, 14(2): 249-251
张婷.2007.黄土丘陵沟壑区安塞纸坊沟流域景观格局及动态研究.[硕士论文西北].陕西杨凌.农林科技大学
张希彪. 2005.黄土丘陵沟壑区土地利用结构的地域分异研究,农业现代化研究, 26(6): 435-439
张霞,孙睿,张兵,童庆禧. 2006.基于MODIS植被指数时间谱的华北平原土地覆盖分类.农业工程学报, 22(12): 128-132
张小飞,王仰麟,李正国,李卫峰. 2004.黄土高原土壤裸露与景观格局关系—以延安市宝塔区为例.第四纪研究, 24(6): 709-715
张小飞,王仰麟,李正国. 2005.基于景观功能网络概念的景观格局优化—以台湾地区乌溪流域典型区为例.生态学报, 25(7): 1707-1713
张晓明,曹文洪,余新晓,武思宏. 2009.黄土丘陵沟壑区典型流域土地利用/覆被变化的径流调节效应.水利学报, 40(6): 641-650
张永红,张继贤,郭健,曹银璇. 2009.全球地图第一版中国土地覆盖产品的生成.武汉大学学报, 34(8): 914-918
张志,金兆,朱清科,张宇清,姚爱静,曹刚,魏铁. 2005.晋西黄土区蔡家川流域景观地形分异格局研究.北京林业大学学报, 27(2): 43-48
赵海霞,李波,刘颖慧,张新时. 2005.皇甫川流域不同尺度景观分异下的土壤性状.生态学报, 25(8): 2010-2018
赵有松,廖安平,陈利军. 2007.国家土地覆盖数据的生产与建库.测绘科学, 32(14): 138-140
郑凯,管磊,刘佳佳. 2009.利用多光谱与合成孔径雷达图像研究土地利用/土地覆盖动态变化.中国
海洋大学学报, 39(Sup.): 445-449
郑利娟,李小娟,胡德勇,周德民. 2009.基于对象和DEM的湿地信息提取—以洪河沼泽湿地为例.遥感技术与应用, 24(3): 346-351
郑毅,武法东,刘艳芳. 2010.一种面向对象分类的特征分析方法.地理与地理信息科学, 26(2): 19-22
中华人民共和国水利部. 1997.土壤侵蚀分类分级标准(SL190-96).北京:中国水利水电出版社, 9- 10
周兴东,于胜文,王志勇,王光彦,刘培. 2007.徐州市遥感图像土地利用分类方法研究.中国水土保持, 4: 57-58
朱颖,李俊祥,孟陈,吴彤,张挺. 2007.上海崇明岛东部近20年土地利用变化.应用生态学报, 18(9): 2040- 2044
朱战强,刘黎明,张军连. 2010.退耕还林对宁南黄土丘陵区景观格局的影响—以中庄村典型小流域为例.生态学报, 30(1): 0146-0154
邹敏,吴泉源,逄杰武. 2007.基于DEM的龙口市土地利用空间格局与时空变化研究.测绘科学, 32(6): 173-175
Ali-Akbar A, Mohammed Ali S, Nanno J M. 2000. Likelihood-based image classification procedures. segmentation and classification:a framework for the integration of expert knowledge in image classification procedures. International Journal of Applied Earth observation and Geoinfbrmation, 2(2): 104-119
Anthony J T, Frank D V. 1997. Predicting the probability of Elevated Nitrate Concentrations in the Puget Sound Basin: Implications for Aquifer Susceptibility and Vulnerability. Ground Water, 35(6): 1029～1039
Baatz M, Hoffmann C, Willhauck G. 2008. Progressing from object-based to object-oriented image analysis. object-based image analysis. Springer, Berlin Heidelberg, 29-42.
Baatz M, schape A. 1999. Object-oriented and multi-scale image analysis in semantic networks In: Proc of the 2nd International Symposium on OperationaIization of Remote Sensing. l6-20th l999. EnschedeITC
Baraldi A, Blonda P. l999. A survey of fuzzy clustering algrithoms for partern recognition part I(Ⅱ). IEEE Transon Systems, Man, and Cybernetics Part B:Cybernetics, 29(6): 778-785
Bastian O. 2000. Landscape classification in Saxony. Landscape Urban Plan, 50: 145-155
Bauer T, Steinnocher K. 2001. Per-parcel land use classification in urban areas applying a rule-based technique. GeoBIT, 6: 12-27
Carmel Y, Kadmon R. 1999. Effects of grazing and topography on long-term vegetation changes in a Mediterranean ecosystem in Israel. Plant Ecology, 145(2): 243-254
Carrere V. 1990. Development of multiple source date processing forst ructural analysis at a regional scale. Photogrammetric Engineering & Remote Sensing, 56: 587-596
Chen L, Wang J, Fu B, Yang Q. 2001. Land-use change in a small catchment of northern Loess Plateau, China. Agriculture, Ecosystems & Environment, 86(2): 163-172
Chen Y, Wei S, Li J, Sun Z. 2009. Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas. Advances in Space Research, 43(7): 1101-1110
Clark D B, Clark D A. 2000. Landscape scale variation in forest structure and biomass in a tropical rain forest. Forest Ecology and Management, 137(123): 243-254
Cleve C, Kelly M, Kearns F R, Moritz M. 2008. Classification of the wildland-urban interface: Acomparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems, 32(4): 317-326
Collatz J, and Randall D A. 1996. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part II: The generation of global fields of terrestrial biophysical parameters from satellite data. Journal of Climate, 9: 706-737
Comber A, Fisher P, Wadsworth R. 2005. What is land cover? Environment and Planning B: Planning and Design. 32(2): 199-209
Definiens. 2005. eCognition Professional, Munich DeFries R S and Townshend J R G. 1994. NDVI-derived land cover classification at global scales. International Journal of Remote Sensing, 15: 3567-3586
Dorrena Luuk K A, Maierb Bernhard, Seijmonsbergena Arie C. 2003. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology and Management, 183: 31-46
ERDAS Field Guide. 1999. 5th Edition ERDAS Inc. Atlanta, Georgia, pp672 ESRI. 2006. ArcGIS 9.2. Redlands, CA, USA: ESRI Inc
Fan J, Gao Y, Luo H, Xu G. 2005. Statistical modeling and conceptualizati on of natural images. Pattern Recogn, 38(6): 865-885
FAO FESLM, 1993. An international framework for evalulating sustainable land management. World Soil Resources Report, Rome, Italy
Fennessy M J and Xue Y. 1997. Impact of vegetation map on GCM seasonal simulations over the United States. Ecological Application, 7(1), 22-33
Florinsky I V, Kuryakova G A. 1996. In fluence of topography on some vegetation cover properties. Catena, 27(2): 123-141
Forman R T T. 1995. Land mosaics: the ecology of landscape and region. New York: Cambridge University Press
Forster D, Buehler Y, Kellenberger T W. 2009. Mapping urban and peri-urban agriculture using high spatial resolution satellite data. Journal of Applied Remote Sensing. Vol 3, 033523
Franklin S E, Wulder M A. 2002. Remote sensing methods in medium spatial resoluti on satellite data land cover classificati on of large areas. Prog Phys Geog, 26(2): 173-205
Fu B J, Zhang Q J, Chen L D, Zhao W W, Gulinck H, Liu G B, Yang Q K, Zhu Y G. 2006. Temporal change in land use and its relationship to slope degree and soil type in a small catchment on the Loess Plateau of China. Catena, 65: 41-48
Fu B and Gulinck H. 1995. Land evaluation in area of severe erosion: The loess plateau of China. Land degradation & Rehabilitatioon, 5(1): 33-40
Gidudu A, Heinz R. 2007. Land cover mapping: optimizing remote sensing data for SVM classification. Journal of Remote Sensing, 11(5): 694-701
Harken J and Sugumaran R. 2005. Classification of Iowa wetlands using an airborne hyper-spectral Image: a comparison of the spectral angle mapper classifier and an object-oriented approach. Canadian Journal of Remote Sensing, 31(2): 167-174
Herzog F, Lausch A, Muller E, Thulke H, Steinhardt U, Lehmann S. 2001. Landscape metrics for assessment of landscape destruction and rehabilitation. Environmental Management, 27(1): 91-107
Hofmann P. 2001.Detection informal settlements from IKONOS image data using methods of object oriented image analysis an example form Cape Town (South Africa). Remote sensing of Urban Areas, Fernerkund-ung in urbanen Raumen. Regensburged. By JURGENS, C: 4l-42.
Huang S, Siegert F. 2006. Land cover classification optimized to detect areas at risk of desertification in North China based on SPOT VEGETATION imagery. Journal of Arid Environments, 67: 308-327
Hulshoff R M. 1995. Landscape indices describing a Dutch landscape. Landscape Ecology, 10: 101-111 Inzana J, Kusky T, Higgs G, Tucker R. 2003. Supervised classifications of landsite TM band ratio images and landsite TM band ratio image with radar for geological interpretations of central madagascar. Journal of African Earth Sciences, 37(1-2): 59-72
Jacquin A, Misakova L, Gay M. 2008. A hybrid object-based classification approach for mapping urban sprawl in periurban environment. Landscape and Urban Planning, 84(2): 152-165
Jyothi Naga B, Babu G R, Murali Krishna I V. 2008. Object oriented and multi-scale image analysis: Strengths, Weaknesses, Opportunities and Threats-A Review. Journal of Computer Science, 4(9): 706-712
Ketting R L and Landgrebe D A. 1976. Computer classification of remotely sensed multispetral image data by extraction and classincation of homogeneous object. IEEE Transactions on Geoscience Electronics, 14(1): 19-26
Kienast F. 1993. Analysis of historic landscape patterns with a geographical infor mation system: A methodological outline. Landscape Ecology, 8: 103-118
Lang S. 2007. Object-based image analysis for remote sensing applications: modeling reality dealing with complexity, bject-based image analysis: spatial concepts for knowledge driven remote sensing app lications. Berlin: Springer, 32-27
Li X, Peterson J, Liu G. 2001. Assessing regional sustainability: the case of land use and land cover change in the middle Yiluo catchment of the Yellow River Basin. Applied Geography, 21(1): 87-106
Li Z, Wang Y,Zhou Q, Wu J, Peng J, Chang H. 2008. Spatiotemporal variability of land surface moisture based on vegetation and temperature characteristics in Northern Shaanxi Loess Plateau, China. Journal of Arid Environments, 72(6): 974-985
Long H L, Heilig G K, Wang J, Li X B, Luo M, Wu X Q, Zhang M. 2006. Land use and soil erosion in the upper reaches of the Yangtze River:some socio-economic considerations on China′s Grain-for-green Programme. Land Degradation & Development, 17(6): 589-603
Loveland T R, Reed B C, Brown J F, Ohlen D O, Zhu Z, Yang L, Merchant J W. 2000. Development of a global land cover characteristics database and IGBP DISCover from 1-KM AVHRR data. Int. J.Remote sens, 21(6-7): 1303-1330
Luo J B, Savakisa E, Singhal A. 2005. A Bayesian Network-based Framework for Semantic Image Understanding. Pattern Recogn, 38(6): 919-934
Mallinis G, Koutsias N, Tsakiri-Strati M, Karteris M. 2008. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing. 63(2):237-250
Mao D, Li W, Lin W. 2010. Remote sensing image classification based on formal concept analysis. Journal of remote sensing, 14(1): 090-103
McGarigal K, Cushman S A, Neel M C, Ene E. 2002. FRAGSTATS 3.3: Spatial pattern analysis programfor categorical maps. Massachusetts: University of Massachusetts, Amherst
Mikk M, Manderü. 1995. Species diversity of forest islands in agricultural landscape of southern Finland, Estonia and Lithuania. Landscape and Urban Planning, 31: 153-169
Miller J R, Joyce L A , Knight R L, King R M. 1996. Forest roads and landscape structure in the southern rocky mountains. Landscape Ecology, 11(2): 115-127
Mokhtarzade M, Ebadi H, Valadan Zoej M J. 2007. Road detection from high-resolution satellite images using artificial neura inetworks. International Journal of Applied Earth observation and Geoinformation, 9(1): 32-40
Mural H, Omatu S. 1997. Remote sensing image analysis using a neural network and knowledge-based processing. Remote Sensing, 18(4): 811-828
Nagaike T, Kamitani T. 1999. Factors affecting changes in landscape diversity in rural areas of the Fagus crenata forest region of central Japan. Landscape and Urban Planning, 43: 209- 216
Ostendor F B, Reynolds J F. 1998. A model of arctic tundra vegetation derived from topographic gradients. Landscape Ecology, 13(3): 187-201
Pearson S M, Turner M G, Drake J B. 1999. Landscape Change and Habitat Availability in the Southern Appalachian Highlands and Olympic Peninsula. Ecological Applications, 9(4): 1288-1304
Gong P, Marceau D J, Howarth P J. 1992. A comparison of spatial feature extraction algorithms for land-use classifjcation with SPOT HRV data. Remote Sensing of Environment, 40(2): 137-151
Pielke R, Lee T J, Copeland J H, Eastman J L, Ziegler C L, Finley C A, 1997. The Use of USGS land surface data sets to improve weather and climate simulations. Ecological Applications, 7(1): 3-21
Qiu J, Zhang, Fu B J, Chen L D, Zhao W W, Yang Q K, Liu G B, Gulinck H. 2004. Dynamics and driving factors of agricultural landscape in the semiarid hilly area of the Loess Plateau, China.Agriculture, Ecosystems and Environment, 103: 535-543
Shrivastava R J and Gebelein J L. 2007. Land cover classification and economic assessment of citrus groves using remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing. 6l(5): 341-353
Thapa R B, Murayama Y. 2009.Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan. Applied Geography. 29: 135-144.
Roberts M R, Wuest L J. 1999. Plant communities of new brunswick in relation to environmental variation. journal of vegetation science, 10(3): 321-334
Jong S M D, Sluider R, Sande C V D, Roo A D. 2001. River flood damage assessment using IKONOS imagery. Natural Hazards Project-Floods, Space AppIication Institute, Joint Research Center European Commission, Ispra
Savard J P L, Clergeaub P, Mennechechez G. 2000. Biodiversity concepts and urban ecosystems. Landscape Urban Plan, 48: 131-142
Sivanpillai R, Latchininsky A V. 2007. Mapping locust habitats in the amudarya river delta, Uzbekistan with multi-temporal MODIS Imagery. Environ Manage, 39:876–886
Stehman S V and Czaplewski R L. 1998. Design and analysis of thematic map accuracy assessment: fundamental principles. Remote Sensing of Environment, 64: 331-344
Tesoriero A J, Voss F D. 1997. Predicting the probability of elevated nitrate concentrations in the Puget Sound basin: Implications for a quifer susceptibility and vulnerability. Ground Water, 35(6): 1029-139
Treitz P and Howarth P. 2000. High spatial reso1ution remote sensing data for forest ecosystemclassification: An Examination of Spatial Scale. Remote Sensing of Environment, 72(3): 268-289
Turner MG, Gardner R H. 1991. Quantitative methods in landscape ecology. New York: Springer-Verlag . Urban D L, Miller C, Halpin P N, Stephenson N L. 2000. Forest gradient response in sierran landscapes: the physical template. Landscape Ecology, 15(7): 603-620
Vogelman J E, Sohl T L, Howard S M. 1998. Regional characterization of land cover using multiple sources of date, Photogrammetric Engineering & Remote Sensing, 64: 45-57
Wang X, Lu C, Fang J, Shen Y. 2007. Implications for development of grain-for-green policy based on crop land suitability evaluation in desertification affected north China. Land Use Policy, 24(2): 417-424
Wang Y L, Zhao Y B, Han D. 1999. The spatial structure of landscape eco-systems: concept, indices and case studies. Advance in Earth Science, 14(3): 235-241
Wilkinson G G. 1996. A Review of current issues in the integration of GIS and remote sensing data. Geographical Information Systems, 10(1): 85-101
Willhauck G, Benz U C, Siegen F. 2002. Semiautomatic classification procedures for nre monitoring using multitemporal SAR images and NOAA-AVHRR hotspot data. Procecdings of the 4th European Confcrence on Synthetic Aperture Radar, Cologne, Germany, 4-6
Stefanov W L, Ramsey M S, Christensen P R. 2001. Monitoring urban Iand cover change: An expert system approach to land cover classincation of semiarid to arid urban centers. Remote sensing of Environment, 77(2): 173-185
Yang L, Weily B K, Tieszen L L, Reed B C. 1998. An analysis of relationships among climate forcing and time integrated NDVI of grasslands over the U. S. Northern and Central Great Plains. Remote Sensing of Environment, 65: 25-37
Zadeh L A. 1965. Fuzzy Sets. Information and Control, 8(3): 338-353
Zhang Q J, Fu B J, Chen L D, Zhao W W, Yang Q K, Liu G B, Gulinck H. 2004. Dynamics and driving factors of agricultural landscape in the semiarid hilly area of the Loess Plateau, China. Agriculture, Ecosystems & Environment, 103 (3): 535-543
Zhu Z, Yang L, Stehman S V, Czaplewski R L. 1999. Designing an accuracy assessment for a USGS regional land cover mapping program. In Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources, K. Lowell and A. Jaton (Eds) (Chelsea, MI: Ann Arbor Press), pp. 393–398
边肇祺,张学工. 2000.模式识别.北京:清华大学出版社
蔡红艳,张树文,张宇博. 2010.全球环境变化视角下的土地覆盖分类系统研究综述.遥感技术与应用, 25(1): 161-167
曹雪,柯长青. 2006.基于对象级的高分辨率遥感影像分类研究.遥感信息, (5): 27-30
陈国建,张晓萍,张平仓,李锐,杨勤科. 2009.基于RS和GIS的六道沟流域土地利用动态变化研究.水土保持研究, 16(2): 95-100
陈靓. 2006.景观生态学研究中的格局分析方法及模型.安庆师范学院学报, 12(3): 13-16
陈静谊,张志强,王新杰,唐丽霞. 2008.黄土高原清水河流域植被景观动态变化研究.水土保持研究, 15(5): 53-56
陈利顶,傅伯杰,王军. 2001.黄土丘陵区典型小流域土地利用变化研究——以陕西延安地区大南沟流域为例.地理科学, 21(1): 46-51
陈利顶,吕一河,傅伯杰. 2006.基于模式识别的景观格局分析与尺度转换研究框架.生态学报, 26 (3): 663-670
陈述彭,赵英时. 1990.遥感地学分析,测绘出版社
陈忠. 2006.高分辨率遥感图像分类技术研究.中国科学院遥感应用研究所,博士论文: 6-7
戴昌达,姜小光,唐伶俐. 2004.遥感图像应用处理与分析.北京:清华大学出版社
邓毅,蔡凌. 2009.基于可持续性的城市景观格局规划概念与方法.城镇景观, 95-101
杜凤兰,田庆久,夏学齐,慧凤鸣. 2004.面向对象的地物分类法分析与评价.遥感技术与应用, 19(1): 20-24
冯春,陈建. 2003.平土地利用/土地覆盖研究中遥感图像分类精度的提高方法.地理与地理信息科学, 19(3): 26-28
傅伯杰,陈利顶,马克明,王仰麟. 2001.景观生态学原理及应用.北京:科学出版社
傅伯杰,陈利顶,马克明. 1999.黄土丘陵区小流域土地利用变化对生态环境的影响.地理学报, 54(3): 241-246
傅伯杰,陈利顶,邱扬. 1988.黄土丘陵沟壑区土地利用结构与生态过程.北京:科学出版社, 185-232
傅伯杰,陈利顶. 1997.土地持续利用评价的指标体系与方法.自然资源学报, 12(2): 112-119
傅伯杰,吕一河,陈利顶,苏常红,姚雪玲,刘宇. 2008.国际景观生态学研究新进展.生态学报, 28(2): 798-804
傅伯杰,马克明,周华峰,陈利顶. 1998.黄土丘陵区土地利用结构对土壤养分分布的影响.科学通报, 43(22): 244-247
傅伯杰. 1995.黄土区穿凿景观空间格局分析.生态学报, 15(2): 113-120
富伟,刘世梁,崔保山,张兆苓. 2009.基于景观格局与过程的云南省典型地区道路网络生态效应.应用生态学报, 20(8): 1925-1931
高欣,常庆瑞,卞德鹏,刘炜,杨昆. 2010.陕北黄土丘陵沟壑区ALOS遥感影像融合方法研究.西北农林科技大学学报, 38(3): 189-193
高杨,吴志峰,刘晓南,张景华. 2008.珠江三角洲景观空间格局分析.热带地理, 28(1): 26-31
高照良,穆兴民. 2004.水土保持学报.黄土水蚀风蚀交错区土地利用覆被时空变化研究—以陕西省神木县六道沟流域为例, 18(5): 146-150
高志强,宁吉才,高炜. 2009.基于遥感的沿海土地利用变化及地表温度响应.农业工程学报, 25(9): 274-281
宫攀,陈仲新,唐华俊,张凤荣,张明伟. 2006.基于MODIS温度/植被指数的东北地区土地覆盖分类.农业工程学报, 22(9): 94-99
宫攀,唐华俊,陈仲新,张凤荣. 2006.基于MODISLST修正NDVI时序数列的土地覆盖分类.资源科学, 28(4): 104-110
宫鹏. 2009.基于全球通量观测站的全球土地覆盖图精度检验.自然科学进展, 19(7): 754-759
顾娟,李新,黄春林. 2010.基于时序MODISNDVI的黑河流域土地覆盖分类研究.地球科学进展, 25(3): 317-326
郭泺,夏北成,刘蔚秋. 2006.地形因子对森林景观格局多尺度效应分析.生态学杂志, 25(8): 900- 904
韩书成,谢永生,濮励杰.2006.黄土高原沟壑区小流域土地利用特征变化分析—以长武王东沟为例.干旱区资源与环境, 20(4): 73-77
郝润梅,海春兴,雷军. 2006.农牧交错带农田景观格局对土地生态环境安全的影响—以呼和浩特市为例.干旱区地理, 29(5): 700-704
郝仕龙,陈南详,柯俊. 2005.黄土丘陵小流域土地利用景观空间格局动态分析.农业工程学报, 21(6): 50-53
何彬方,范伟,刘惠敏,冯妍. 2007.基于MODIS数据的安徽省土地覆盖分类研究.红外技术, 29(11): 651-656
何东进,洪滔,胡海清,王英姿,刘勇生,范圣锋. 2007.武夷山风景名胜区不同森林景观物种多样性特征研究.中国生态农业学报, 15(2): 9-13
侯伟,鲁学军,张春晓,王静. 2010.面向对象的高分辨率影像信息提取方法研究—以四川理县居民地提取为例.地球信息科学学报, 12(1): 119-125
胡宝清,金姝兰,曹少英,蒋树芳. 2004.基于GIS技术的广西喀斯特生态环境脆弱性综合评价.水土保持学报, 18(1): 103-107
胡相明,程积民,万惠娥,赵艳云. 2006.黄土丘陵区地形、土壤水分与草地的景观格局.生态学报, 26 (10): 3276-3285
黄慧萍,吴炳方,李苗苗,周为峰,王忠武. 2004.高分辨率影像城市绿地快速提取技术与应用.遥感学报, 8(1): 68-74
黄慧萍. 2003.面向对象影像分析中的尺度问题研究.北京:中国科学院研究生院
黄奕龙,陈利顶,傅伯杰,王仰麟. 2005.黄土丘陵小流域土壤水分空间格局及其影响因素.自然资源学报, 20(4): 483-492
贾宁凤,段建南,乔志敏. 2007.土地利用空间分布与地形因子相关性分析方法.经济地理, 27(2): 310-312
贾永红,张春森,王爱平. 2001.基于BP神经网络的多源遥感影像分类.西安科技学院学报, 21(1): 58-60
蒋定生. 1997.黄土高原水土流失与治理模式.中国水利水电出版社
琚彤军,刘普灵. 2001.燕沟流域水保治理措施优化配置示范研究.水土保持学报, 15(5): 55-57
琚彤军,王建军,刘普灵,郑世清. 2002.燕沟示范区生态农业快速规模化建设途径分析.西北农林科技大学学报, 2(5): 9-13
孔宁宁,曾辉,李书娟. 2002.四川卧龙自然保护区植被的地形分异格局研究.北京大学学报, 38(4), 543-549
李爱农,江小波,马泽忠,周爱霞. 2003.遥感自动分类在西南地区土地利用调查中的应用研究.遥感技术与应用, 18(5): 282-285
李斌,张金屯. 2009.黄土高原灌丛景观斑块形状的指数和分形分析.中国农学通报, 25(22): 296-300
李朝峰. 2004.基于知识发现和决策规则的遥感图像城区土地覆盖/利用分类方法.计算机工程与应用, 23: 212-215
李春华,沙晋明. 2006.基于知识的遥感影像的BAYES分类方法研究—以福州市琅歧岛土地覆盖/土地利用类型为例.水土保持研究, 13(6): 126-128
李敏,崔世勇,李成名,印洁,李云岭. 2008.面向对象的高分辨率遥感影像信息提取—以耕地提取为例.遥感应用, 6:63-89
李团胜. 2005.基于遥感数据的晋陕蒙交汇区景观格局定量分析—以东胜幅1/25万T M影像分析为例.生态学杂志, 24(9): 1033-1037
李小利,刘国彬,张晓萍,孟庆香. 2008.黄土丘陵油田开发区景观破碎化分析.中国水土保持科学, 6(3): 53-58
林皓波,柏延臣,王锦地,贾秀萍,尤淑撑. 2009.基于景观结构的遥感影像超分辨率制图.自然科学进展, 19(12): 1355-1364
林洋.2009.燕沟流域景观格局现状、功能与动态研究. [硕士论文西北].陕西杨凌.农林科技大学
林洋,张文辉. 2010.黄土丘陵沟壑燕沟流域景观构成及功能类型.西北农林科技大学学报, 38(1): 145-160
刘爱霞,王静,吕春艳. 2006.基于MODIS数据的北京西北部地区土地覆盖分类研究.地理科学进展, 25(2): 96-102
刘常娟,杨敏华,张秀英. 2009.面向对象的分类方法在土地利用数据库建设中的应用.测绘科学, 34(1): 176-177
刘芳,郝玉光,张景波,汪守海. 2009.磴口县土地景观变化遥感监测研究.内蒙古农业大学学报, 30(3): 112-116
刘纪远. 1996.中国资源环境遥感宏观调查与动态研究,中国科学技术出版社
刘建飞,杨勤科,梁伟,任宗萍,胡志瑞. 2009.近30年来陕北黄土高原土地利用动态变化分析.水土保持研究, 16(2): 112-116
刘璞,张远,周斌,吴嘉平. 2009.基于SAM和多源信息的土地利用/覆盖自动分类.浙江大学学报, 43(9): 1574-1579
刘普灵,郑世清,琚彤军,王栓全,徐勇. 2007.黄土高原燕沟流域生态环境建设模式及效益研究.中国生态农业学报, 15(3): 175-178
刘庆凤,刘吉平,宋开山. 2010.基于MODIS/NDVI时序数据的土地覆盖分类.中国科学院研究生院学报, 27(2): 163-169
刘仁钊,廖文峰. 2005.遥感图像分类应用研究综述.地理空间信息, 3(5): 1l-13
刘伟,冯学智,肖鹏峰,徐海根. 2006.基于MODIS数据的生物保护区域土地覆被分类.江西师范大学学报, 30(2): 201-205
刘伟,高艳红,李海英,冉有华,程国栋, Chen F. 2007.黑河流域土地覆盖分类数据的建立及其影响的模拟.高原气象, 26(4): 278-285
刘文娟,常庆瑞,贾科利,刘京,刘梦云. 2007.荒漠化地区土地覆被光谱特征与遥感信息提取方法研究.西北农林科技大学学报, 35(9): 205-209
刘文耀,李昆. 1999.南涧干热河谷退化山地植被恢复重建的研究.北京林业大学学报, 21(3): 9-13
刘贤赵,谭春英. 2005.黄土高原沟壑区典型小流域土地利用变化对产水量的影响—以陕西省长武王东沟流域为例.中国生态农业学报, 13(4): 99-102
刘玉洁,杨忠东. 2001. MODIS遥感信息处理原理与算法.科学出版社
刘志刚,史文中,李德仁,秦前清. 2005.一种基于支撑向量机的遥感影像不完全监督分类新方法.遥感学报, 9(4): 363-373
梁伟,杨勤科,刘咏梅. 2006.黄土高原地形复杂区土地利用信息提取方法研究.国土资源遥感, 17(3): 56-60
陆大明,范建友. 2007.基于面向对象影像分类技术的生态清洁小流域现状调查.中国水土保持, 9: 23-26
罗海江,白海玲,王文杰,申文明,张峰,刘小曼. 2006.面向生态监测与管理的国家级土地生态分类方案研究.中国环境监测, 22(10): 57-60
罗来平. 2006.遥感图像分类中模糊模式识别和决策树方法的应用研究.北京:首都师范大学
骆成凤,刘正军,王长耀,牛铮. 2006.基于遗传算法优化的BP神经网络遥感数据土地覆盖分类.农业工程学报, 22(12): 133-137
骆剑承,周成虎,梁怡,马江洪. 2002.支撑向量机及其遥感影像空间特征提取和分类的应用研究,遥感学报, 6(1): 50-55
买凯乐,张文辉. 2011a.黄土丘陵沟壑区遥感影像信息面向对象分类方法提取.农业机械学, 42(4): 169-175
买凯乐,张文辉. 2011b.基于ALOS影像的黄土丘陵区典型流域景观空间格局.应用生态学报, 22(3): 741-747
孟平,贾宝全,张劲松,高峻. 2004.太行山低山丘陵区景观变化特征分析—以河南省济源市为例.生态学报, 24(4): 825-830
莫登奎,林辉,孙华,熊育久,刘秀英. 2005.基于高分辨率遥感影像的土地覆盖信息提取.遥感技术与应用, 20(4): 411-414
莫源富,周立新. 2000.分区分类法针对山区遥感图象的一种有效的分类方法,中国岩溶, 19(4): 360-365
潘竟虎. 2010.基于光谱混合模型的城市绿色空间格局分析—以兰州市为例.城市问题, 2: 18-23
彭海涛,柯长青. 2010.基于多层分割的面向对象遥感影像分类方法研究.遥感技术与应用, 25(1): 149-154
彭望碌,白振平,刘湘南,曹彤. 2002.遥感概论.北京:高等教育出版社
蒲智,刘萍,杨辽,葛挺进. 2006.面向对象技术在城市绿地信息提取中的应用.福建林业科, 33(1): 40-44
邱扬,张金屯. 2000. DCCA排序轴分类及其在关帝山八水沟植物群落生态梯度分析中的应用.生态学报, 20(2): 199-206
Roberto V,周月琴. 1997.影像理解的智能系统.测绘通报, 1: 40-41
容丽,王世杰,杜雪莲. 2006.喀斯特低热河谷石漠化区环境梯度的小气候效应—以贵州花江峡谷区小流域为例.生态学杂志, 25(9): 1038-1043
申广荣,钱振华,徐敬敬,李艳. 2009.基于eCognition的城镇绿地信息动态监测研究.上海交通大学学报, 27(1): 1-6
沈泽昊,赵俊. 2007.基于植物-地形关系的物种丰富度空间格局预测—GAMs途径的一种应用.生态学报, 27(3): 953-963生态学名词审定委员会. 2006.生态学名词.北京:科学出版社
宋冬梅,肖笃宁,张志诚,曹宇,马明国,王建华,王建. 2003.甘肃民勤绿洲的景观格局变化及驱动力分析.应用生态学报, 14(4): 535-539
宋永昌. 2001.植被生态学.上海:华东师范大学出版社
孙丹峰,杨冀红,刘顺喜. 2002.高分辨率遥感卫星影像在土地利用分类及其变化监测的应用研究.农业工程学报. 18(2): 160-164
孙建国,杨树文,段焕娥,王沛,闫浩文. 2009.基于光谱和纹理特征的山区高分辨率遥感影像分类.测绘科学, 3(6): 92-93
孙艳玲,杨小唤,王新生,陈建军. 2007.基于决策树和MODIS数据的土地利用分类.资源科学, 29(5): 169-174
孙艳敏,林培. 2005.县级中大比例尺以土地类型划分为基础进行农用地分等的方法论研究—以太原市万柏林区为例.中国农业科学, 38(7): 1408-1413
索安宁,熊友才,王天明,寇晓军,葛剑平. 2007.黄土高原子午岭森林破碎化对流域水文过程的影响.林业科学, 43(6): 13-19
索安宁,周睿,洪军,葛剑平. 2005.基于遥感和GIS的黄土高原小流域植被退化研究.北京师范大学学报, 41(2): 181-184
覃先林,增元,易浩若. 2005.高空间分辨率卫星遥感影像树冠信息提取方法研究.遥感技术与应用, 20(2): 228-232
田均良,刘普灵,张翼. 2000.治理水土流失再造山川秀美延安.水土保持研究, 7(2):4-9
田新光. 2007.面向对象高分辨率遥感影像信息提取.中国测绘科学研究院
田野,方涛. 2010.基于ART2技术的面向对象高分辨遥感影像分类.计算机应用与软件, 27(4): 48-50
万幼川,宋杨. 2005.基于高分辨率遥感影像分类的地图更新方法.武汉大学学报, 30(2): 105-109
汪闽,万其明,张大骞,张青峰. 2010.光谱、形状特征结合的多精度图像分割算法与应用.地球信息科学学报, 12(2): 261-268
王长耀,牛铮,唐华俊. 2001.对地观测技术与精细农业.科学出版社
王惠英,苏伟,周军其. 2009.基于多分类器组合面向对象遥感影像信息提取方法.测绘工程, 18(5): 22-26
王莉雯,牛铮,卫亚星. 2007.基于MODISNDVI的新疆土地覆盖类型划分与潜在荒漠化区域探测.干旱区地理, 30(4): 573-578
王礼先. 1999.流域管理学.北京:中国林业出版社
王文宇,李博. 2006.基于eCogniton的高分辨率遥感图像的自动识别分类技术.北京建筑工程学院学报, 22(4): 26-29
王晓燕,徐志高,杨明义,田均良. 2004.黄土高原小流域景观多样性动态分析.应用生态学报, 15(2): 273-277
王永军,李团胜. 2006.基于GIS的榆林地区景观格局动态变化.生态学杂志, 25(8): 859-899
瓦普尼克. 2000.统计学习理论的本质.北京:清华大学出版社
魏静,郑小刚,葛京凤. 2007.石家庄西部太行山区景观格局时空变化.生态学报, 27(5): 1993-2001
温仲明,焦峰,张晓萍,杨勤科. 2004.纸坊沟流域近60年来土地利用景观变化的环境效应.生态学报, 24(9): 1903-1909
温仲明,焦峰,赫晓慧,杨勤科,刘宝元. 2006.纸坊沟流域黄土丘陵区土地生产力变化与生态环境改善.农业工程学报. 22(8): 91-95
温仲明,焦峰,张晓萍,杨勤科. 2004a.黄土丘陵区纸坊沟流域60年来土地利用格局变化研究.水土保持学报, 18(5): 125-133
温仲明,焦峰,张晓萍,杨勤科. 2004b.纸坊沟流域近60年来土地利用景观变化的环境效应.生态学报, 24(9): 1903-1909
邬建国. 2000a.景观生态学—格局、过程、尺度与等级.北京:高等教育出版社, 19-31
邬建国. 2000b.景观生态学—概念与理论.生态学杂志, 19(1): 42-52
吴连喜,饶月辉. 2009.一种用于土地利用调查的颜色保真的遥感图像融合方法.土壤通报, 40(4): 731-735
吴文斌,杨鹏,张莉,唐华俊,周清波, Shibasaki R. 2009.四类全球土地覆盖数据在中国区域的精度
评价.农业工程学报, 25(12): 167-173
夏俊士,杜培军,张海荣,刘培. 2010.基于遥感数据的城市地表温度与土地覆盖定量研究.遥感技术与应用, 25(1): 15-23
肖笃宁. 1991.景观生态学:理论、方法及应用.北京:中国林业出版社
谢芳,邱国玉,尹婧,熊育久,王佩. 2009.泾河流域40年的土地利用/覆盖变化分区对比研究.自然资源学报, 24(8): 1354-1365
徐永明,刘勇洪,魏鸣,吕晶晶. 2007.基于MODIS数据的长江三角洲地区土地覆盖分类.地理学报, 62(6): 640-648
徐勇, Roy C S. 2001.黄土高原丘陵区燕沟流域土地利用变化与优化调控.地理学报, 56(6): 175-178
徐勇,马定国,郭腾云. 2004.黄土丘陵区的生态环境建设宜走“梯田退耕”的路子.水土保持研究, 11(3): 112-115
徐勇,田均良,刘普灵. 2002.黄土高原中部丘陵区生态适宜型农村经济发展模式案例研究.水土保持通报, 22(3): 43-46
徐勇,田均良,刘普灵. 2004.黄土丘陵区梯田退耕生态重建规划方法—以燕沟流域为例.自然资源学报, 19(5): 637-646
徐勇,许炯心,房金福. 2000.黄上高原中部丘陵区农村经济特征、制约因素与发展对策.水十保持研究, 7(2): 14-21
许晓波,张婷,张文辉. 2009.纸坊沟流域近25年景观斑块功能类型及动态.林业科学, 45(5): 154-160
薛莲,金卫斌,熊勤学,刘章勇. 2010.基于MODIS和ENVISAT数据的湖北省四湖地区土地覆盖分类.应用生态学报, 21(3): 791-795
薛智德,杨光,梁一民. 1997.燕沟人工植被营造模式与快速建设研究.水土保持研究, 7(2): 128-132
严海英. 2008. QUICKBIRD影像用于城市用地信息提取方法.测绘科学, 33(2): 216-217
杨娟,葛剑平,洪军. 2006.卧龙地区流域土地覆盖变化及其对大熊猫潜在生境的影响.生态学报, 26(6): 1975-1980
杨朴,史明昌,黄兆伟,李团宏. 2009.面向对象的黄土高原坝系拓扑关系数据模型研究.中国水土保持, 5: 36-43
杨叶涛,龚建雅,周启鸣,王迎迎. 2010.土地利用景观格局对城市扩张影响研究.自然资源学报,25(2): 320-329
姚凤梅,张佳华. 2006.全球及区域模式中陆面过程的地表植被覆盖分类方法.干旱区地理, 29(6): 872-877
姚静,武文波,康停军. 2010.基于TM影像的城市绿地信息提取方法研究.测绘科学, 35(1): 113-115
游珍,李占斌. 2007.小流域人类活动的时空分异的景观格局特征—以纸坊沟流域为例.干旱区资源与环境, 21(3): 89-94
余新晓,张晓明,牛丽丽,岳永杰,武思宏,张满良. 2009.黄土高原流域土地利用/覆被动态演变及驱动力分析.农业工程学报, 25(7): 219-225
俞来雷. 2010.国家历史文化名城土地覆盖研究.城市问题, 4: 15-62
张翠萍,牛建明,董建军,李旻. 2006.基于IKONOS数据的植被制图与植被空间格局—以五分地沟试验区为例.生态学报, 26(2): 449-456
张洪亮. 2000.一种基于GIS的森林分类专家系统(FCGES)理论与方法.云南地理环境研究. 12(1): 54-58
张锦水,潘耀忠,韩立建,苏伟,何春阳. 2007.光谱与纹理信息复合的土地利用/覆盖变化动态监测研究.遥感学报, 11(4): 500-510
张景,姚凤梅,徐永明,张佳华. 2010.基于MODIS的土地覆盖遥感分类特征的评价与比较.地理科学, 30(2): 248-253
张路,廖明生. 2006.一种顾及上下文的遥感影像模糊聚类.遥感学报, 10(1): 58-61
张明阳,王克林,何萍,谢志仁,高吉喜. 2005.白洋淀流域景观空间格局变化研究.资源科学, 27(2): 134-140
张秋玲,马金辉,赵传燕. 2007.兴隆山地区景观格局变化及驱动因子.生态学报, 27(8): 3206-3214
张树华,王百田. 2007.晋西黄土丘陵县域景观格局分析.水土保持研究, 14(2): 249-251
张婷.2007.黄土丘陵沟壑区安塞纸坊沟流域景观格局及动态研究.[硕士论文西北].陕西杨凌.农林科技大学
张希彪. 2005.黄土丘陵沟壑区土地利用结构的地域分异研究,农业现代化研究, 26(6): 435-439
张霞,孙睿,张兵,童庆禧. 2006.基于MODIS植被指数时间谱的华北平原土地覆盖分类.农业工程学报, 22(12): 128-132
张小飞,王仰麟,李正国,李卫峰. 2004.黄土高原土壤裸露与景观格局关系—以延安市宝塔区为例.第四纪研究, 24(6): 709-715
张小飞,王仰麟,李正国. 2005.基于景观功能网络概念的景观格局优化—以台湾地区乌溪流域典型区为例.生态学报, 25(7): 1707-1713
张晓明,曹文洪,余新晓,武思宏. 2009.黄土丘陵沟壑区典型流域土地利用/覆被变化的径流调节效应.水利学报, 40(6): 641-650
张永红,张继贤,郭健,曹银璇. 2009.全球地图第一版中国土地覆盖产品的生成.武汉大学学报, 34(8): 914-918
张志,金兆,朱清科,张宇清,姚爱静,曹刚,魏铁. 2005.晋西黄土区蔡家川流域景观地形分异格局研究.北京林业大学学报, 27(2): 43-48
赵海霞,李波,刘颖慧,张新时. 2005.皇甫川流域不同尺度景观分异下的土壤性状.生态学报, 25(8): 2010-2018
赵有松,廖安平,陈利军. 2007.国家土地覆盖数据的生产与建库.测绘科学, 32(14): 138-140
郑凯,管磊,刘佳佳. 2009.利用多光谱与合成孔径雷达图像研究土地利用/土地覆盖动态变化.中国
海洋大学学报, 39(Sup.): 445-449
郑利娟,李小娟,胡德勇,周德民. 2009.基于对象和DEM的湿地信息提取—以洪河沼泽湿地为例.遥感技术与应用, 24(3): 346-351
郑毅,武法东,刘艳芳. 2010.一种面向对象分类的特征分析方法.地理与地理信息科学, 26(2): 19-22
中华人民共和国水利部. 1997.土壤侵蚀分类分级标准(SL190-96).北京:中国水利水电出版社, 9- 10
周兴东,于胜文,王志勇,王光彦,刘培. 2007.徐州市遥感图像土地利用分类方法研究.中国水土保持, 4: 57-58
朱颖,李俊祥,孟陈,吴彤,张挺. 2007.上海崇明岛东部近20年土地利用变化.应用生态学报, 18(9): 2040- 2044
朱战强,刘黎明,张军连. 2010.退耕还林对宁南黄土丘陵区景观格局的影响—以中庄村典型小流域为例.生态学报, 30(1): 0146-0154
邹敏,吴泉源,逄杰武. 2007.基于DEM的龙口市土地利用空间格局与时空变化研究.测绘科学, 32(6): 173-175
Ali-Akbar A, Mohammed Ali S, Nanno J M. 2000. Likelihood-based image classification procedures. segmentation and classification:a framework for the integration of expert knowledge in image classification procedures. International Journal of Applied Earth observation and Geoinfbrmation, 2(2): 104-119
Anthony J T, Frank D V. 1997. Predicting the probability of Elevated Nitrate Concentrations in the Puget Sound Basin: Implications for Aquifer Susceptibility and Vulnerability. Ground Water, 35(6): 1029～1039
Baatz M, Hoffmann C, Willhauck G. 2008. Progressing from object-based to object-oriented image analysis. object-based image analysis. Springer, Berlin Heidelberg, 29-42.
Baatz M, schape A. 1999. Object-oriented and multi-scale image analysis in semantic networks In: Proc of the 2nd International Symposium on OperationaIization of Remote Sensing. l6-20th l999. EnschedeITC
Baraldi A, Blonda P. l999. A survey of fuzzy clustering algrithoms for partern recognition part I(Ⅱ). IEEE Transon Systems, Man, and Cybernetics Part B:Cybernetics, 29(6): 778-785
Bastian O. 2000. Landscape classification in Saxony. Landscape Urban Plan, 50: 145-155
Bauer T, Steinnocher K. 2001. Per-parcel land use classification in urban areas applying a rule-based technique. GeoBIT, 6: 12-27
Carmel Y, Kadmon R. 1999. Effects of grazing and topography on long-term vegetation changes in a Mediterranean ecosystem in Israel. Plant Ecology, 145(2): 243-254
Carrere V. 1990. Development of multiple source date processing forst ructural analysis at a regional scale. Photogrammetric Engineering & Remote Sensing, 56: 587-596
Chen L, Wang J, Fu B, Yang Q. 2001. Land-use change in a small catchment of northern Loess Plateau, China. Agriculture, Ecosystems & Environment, 86(2): 163-172
Chen Y, Wei S, Li J, Sun Z. 2009. Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas. Advances in Space Research, 43(7): 1101-1110
Clark D B, Clark D A. 2000. Landscape scale variation in forest structure and biomass in a tropical rain forest. Forest Ecology and Management, 137(123): 243-254
Cleve C, Kelly M, Kearns F R, Moritz M. 2008. Classification of the wildland-urban interface: Acomparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems, 32(4): 317-326
Collatz J, and Randall D A. 1996. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part II: The generation of global fields of terrestrial biophysical parameters from satellite data. Journal of Climate, 9: 706-737
Comber A, Fisher P, Wadsworth R. 2005. What is land cover? Environment and Planning B: Planning and Design. 32(2): 199-209
Definiens. 2005. eCognition Professional, Munich DeFries R S and Townshend J R G. 1994. NDVI-derived land cover classification at global scales. International Journal of Remote Sensing, 15: 3567-3586
Dorrena Luuk K A, Maierb Bernhard, Seijmonsbergena Arie C. 2003. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology and Management, 183: 31-46
ERDAS Field Guide. 1999. 5th Edition ERDAS Inc. Atlanta, Georgia, pp672 ESRI. 2006. ArcGIS 9.2. Redlands, CA, USA: ESRI Inc
Fan J, Gao Y, Luo H, Xu G. 2005. Statistical modeling and conceptualizati on of natural images. Pattern Recogn, 38(6): 865-885
FAO FESLM, 1993. An international framework for evalulating sustainable land management. World Soil Resources Report, Rome, Italy
Fennessy M J and Xue Y. 1997. Impact of vegetation map on GCM seasonal simulations over the United States. Ecological Application, 7(1), 22-33
Florinsky I V, Kuryakova G A. 1996. In fluence of topography on some vegetation cover properties. Catena, 27(2): 123-141
Forman R T T. 1995. Land mosaics: the ecology of landscape and region. New York: Cambridge University Press
Forster D, Buehler Y, Kellenberger T W. 2009. Mapping urban and peri-urban agriculture using high spatial resolution satellite data. Journal of Applied Remote Sensing. Vol 3, 033523
Franklin S E, Wulder M A. 2002. Remote sensing methods in medium spatial resoluti on satellite data land cover classificati on of large areas. Prog Phys Geog, 26(2): 173-205
Fu B J, Zhang Q J, Chen L D, Zhao W W, Gulinck H, Liu G B, Yang Q K, Zhu Y G. 2006. Temporal change in land use and its relationship to slope degree and soil type in a small catchment on the Loess Plateau of China. Catena, 65: 41-48
Fu B and Gulinck H. 1995. Land evaluation in area of severe erosion: The loess plateau of China. Land degradation & Rehabilitatioon, 5(1): 33-40
Gidudu A, Heinz R. 2007. Land cover mapping: optimizing remote sensing data for SVM classification. Journal of Remote Sensing, 11(5): 694-701
Harken J and Sugumaran R. 2005. Classification of Iowa wetlands using an airborne hyper-spectral Image: a comparison of the spectral angle mapper classifier and an object-oriented approach. Canadian Journal of Remote Sensing, 31(2): 167-174
Herzog F, Lausch A, Muller E, Thulke H, Steinhardt U, Lehmann S. 2001. Landscape metrics for assessment of landscape destruction and rehabilitation. Environmental Management, 27(1): 91-107
Hofmann P. 2001.Detection informal settlements from IKONOS image data using methods of object oriented image analysis an example form Cape Town (South Africa). Remote sensing of Urban Areas, Fernerkund-ung in urbanen Raumen. Regensburged. By JURGENS, C: 4l-42.
Huang S, Siegert F. 2006. Land cover classification optimized to detect areas at risk of desertification in North China based on SPOT VEGETATION imagery. Journal of Arid Environments, 67: 308-327
Hulshoff R M. 1995. Landscape indices describing a Dutch landscape. Landscape Ecology, 10: 101-111 Inzana J, Kusky T, Higgs G, Tucker R. 2003. Supervised classifications of landsite TM band ratio images and landsite TM band ratio image with radar for geological interpretations of central madagascar. Journal of African Earth Sciences, 37(1-2): 59-72
Jacquin A, Misakova L, Gay M. 2008. A hybrid object-based classification approach for mapping urban sprawl in periurban environment. Landscape and Urban Planning, 84(2): 152-165
Jyothi Naga B, Babu G R, Murali Krishna I V. 2008. Object oriented and multi-scale image analysis: Strengths, Weaknesses, Opportunities and Threats-A Review. Journal of Computer Science, 4(9): 706-712
Ketting R L and Landgrebe D A. 1976. Computer classification of remotely sensed multispetral image data by extraction and classincation of homogeneous object. IEEE Transactions on Geoscience Electronics, 14(1): 19-26
Kienast F. 1993. Analysis of historic landscape patterns with a geographical infor mation system: A methodological outline. Landscape Ecology, 8: 103-118
Lang S. 2007. Object-based image analysis for remote sensing applications: modeling reality dealing with complexity, bject-based image analysis: spatial concepts for knowledge driven remote sensing app lications. Berlin: Springer, 32-27
Li X, Peterson J, Liu G. 2001. Assessing regional sustainability: the case of land use and land cover change in the middle Yiluo catchment of the Yellow River Basin. Applied Geography, 21(1): 87-106
Li Z, Wang Y,Zhou Q, Wu J, Peng J, Chang H. 2008. Spatiotemporal variability of land surface moisture based on vegetation and temperature characteristics in Northern Shaanxi Loess Plateau, China. Journal of Arid Environments, 72(6): 974-985
Long H L, Heilig G K, Wang J, Li X B, Luo M, Wu X Q, Zhang M. 2006. Land use and soil erosion in the upper reaches of the Yangtze River:some socio-economic considerations on China′s Grain-for-green Programme. Land Degradation & Development, 17(6): 589-603
Loveland T R, Reed B C, Brown J F, Ohlen D O, Zhu Z, Yang L, Merchant J W. 2000. Development of a global land cover characteristics database and IGBP DISCover from 1-KM AVHRR data. Int. J.Remote sens, 21(6-7): 1303-1330
Luo J B, Savakisa E, Singhal A. 2005. A Bayesian Network-based Framework for Semantic Image Understanding. Pattern Recogn, 38(6): 919-934
Mallinis G, Koutsias N, Tsakiri-Strati M, Karteris M. 2008. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing. 63(2):237-250
Mao D, Li W, Lin W. 2010. Remote sensing image classification based on formal concept analysis. Journal of remote sensing, 14(1): 090-103
McGarigal K, Cushman S A, Neel M C, Ene E. 2002. FRAGSTATS 3.3: Spatial pattern analysis programfor categorical maps. Massachusetts: University of Massachusetts, Amherst
Mikk M, Manderü. 1995. Species diversity of forest islands in agricultural landscape of southern Finland, Estonia and Lithuania. Landscape and Urban Planning, 31: 153-169
Miller J R, Joyce L A , Knight R L, King R M. 1996. Forest roads and landscape structure in the southern rocky mountains. Landscape Ecology, 11(2): 115-127
Mokhtarzade M, Ebadi H, Valadan Zoej M J. 2007. Road detection from high-resolution satellite images using artificial neura inetworks. International Journal of Applied Earth observation and Geoinformation, 9(1): 32-40
Mural H, Omatu S. 1997. Remote sensing image analysis using a neural network and knowledge-based processing. Remote Sensing, 18(4): 811-828
Nagaike T, Kamitani T. 1999. Factors affecting changes in landscape diversity in rural areas of the Fagus crenata forest region of central Japan. Landscape and Urban Planning, 43: 209- 216
Ostendor F B, Reynolds J F. 1998. A model of arctic tundra vegetation derived from topographic gradients. Landscape Ecology, 13(3): 187-201
Pearson S M, Turner M G, Drake J B. 1999. Landscape Change and Habitat Availability in the Southern Appalachian Highlands and Olympic Peninsula. Ecological Applications, 9(4): 1288-1304
Gong P, Marceau D J, Howarth P J. 1992. A comparison of spatial feature extraction algorithms for land-use classifjcation with SPOT HRV data. Remote Sensing of Environment, 40(2): 137-151
Pielke R, Lee T J, Copeland J H, Eastman J L, Ziegler C L, Finley C A, 1997. The Use of USGS land surface data sets to improve weather and climate simulations. Ecological Applications, 7(1): 3-21
Qiu J, Zhang, Fu B J, Chen L D, Zhao W W, Yang Q K, Liu G B, Gulinck H. 2004. Dynamics and driving factors of agricultural landscape in the semiarid hilly area of the Loess Plateau, China.Agriculture, Ecosystems and Environment, 103: 535-543
Shrivastava R J and Gebelein J L. 2007. Land cover classification and economic assessment of citrus groves using remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing. 6l(5): 341-353
Thapa R B, Murayama Y. 2009.Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan. Applied Geography. 29: 135-144.
Roberts M R, Wuest L J. 1999. Plant communities of new brunswick in relation to environmental variation. journal of vegetation science, 10(3): 321-334
Jong S M D, Sluider R, Sande C V D, Roo A D. 2001. River flood damage assessment using IKONOS imagery. Natural Hazards Project-Floods, Space AppIication Institute, Joint Research Center European Commission, Ispra
Savard J P L, Clergeaub P, Mennechechez G. 2000. Biodiversity concepts and urban ecosystems. Landscape Urban Plan, 48: 131-142
Sivanpillai R, Latchininsky A V. 2007. Mapping locust habitats in the amudarya river delta, Uzbekistan with multi-temporal MODIS Imagery. Environ Manage, 39:876–886
Stehman S V and Czaplewski R L. 1998. Design and analysis of thematic map accuracy assessment: fundamental principles. Remote Sensing of Environment, 64: 331-344
Tesoriero A J, Voss F D. 1997. Predicting the probability of elevated nitrate concentrations in the Puget Sound basin: Implications for a quifer susceptibility and vulnerability. Ground Water, 35(6): 1029-139
Treitz P and Howarth P. 2000. High spatial reso1ution remote sensing data for forest ecosystemclassification: An Examination of Spatial Scale. Remote Sensing of Environment, 72(3): 268-289
Turner MG, Gardner R H. 1991. Quantitative methods in landscape ecology. New York: Springer-Verlag . Urban D L, Miller C, Halpin P N, Stephenson N L. 2000. Forest gradient response in sierran landscapes: the physical template. Landscape Ecology, 15(7): 603-620
Vogelman J E, Sohl T L, Howard S M. 1998. Regional characterization of land cover using multiple sources of date, Photogrammetric Engineering & Remote Sensing, 64: 45-57
Wang X, Lu C, Fang J, Shen Y. 2007. Implications for development of grain-for-green policy based on crop land suitability evaluation in desertification affected north China. Land Use Policy, 24(2): 417-424
Wang Y L, Zhao Y B, Han D. 1999. The spatial structure of landscape eco-systems: concept, indices and case studies. Advance in Earth Science, 14(3): 235-241
Wilkinson G G. 1996. A Review of current issues in the integration of GIS and remote sensing data. Geographical Information Systems, 10(1): 85-101
Willhauck G, Benz U C, Siegen F. 2002. Semiautomatic classification procedures for nre monitoring using multitemporal SAR images and NOAA-AVHRR hotspot data. Procecdings of the 4th European Confcrence on Synthetic Aperture Radar, Cologne, Germany, 4-6
Stefanov W L, Ramsey M S, Christensen P R. 2001. Monitoring urban Iand cover change: An expert system approach to land cover classincation of semiarid to arid urban centers. Remote sensing of Environment, 77(2): 173-185
Yang L, Weily B K, Tieszen L L, Reed B C. 1998. An analysis of relationships among climate forcing and time integrated NDVI of grasslands over the U. S. Northern and Central Great Plains. Remote Sensing of Environment, 65: 25-37
Zadeh L A. 1965. Fuzzy Sets. Information and Control, 8(3): 338-353
Zhang Q J, Fu B J, Chen L D, Zhao W W, Yang Q K, Liu G B, Gulinck H. 2004. Dynamics and driving factors of agricultural landscape in the semiarid hilly area of the Loess Plateau, China. Agriculture, Ecosystems & Environment, 103 (3): 535-543
Zhu Z, Yang L, Stehman S V, Czaplewski R L. 1999. Designing an accuracy assessment for a USGS regional land cover mapping program. In Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources, K. Lowell and A. Jaton (Eds) (Chelsea, MI: Ann Arbor Press), pp. 393–398
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